One of the most common methods of scientific inquiry in demographic research in the sample survey, which permits inference to large diverse natural populations. One theoretical foundation of that inference rests on complete measurement of all sample units; that is, the absence of unit nonresponse. When sample persons who fail to participate differ on key health-related variables from those who do participate, nonresponse error can threaten inference both for descriptive and structural model-based estimates. The efficacy both of efforts to reduce nonresponse through data collection strategies and of statistical postsurvey adjustment for nonresponse rest on (at least implicit) theories of survey participation. The proposed project builds on four years of prior investigation of nonresponse features of several large U.S. national household surveys and the theoretical principles inductively derived from qualitative investigations of survey participation. Based on that work the investigators have encouraged the enrichment of observational and other measures of attributes of the interviewer, neighborhood, household, sample person, and features of the interaction between interviewer and sample person. The collection of these data is specified at the design stage of a survey by a designer who anticipates the fact that nonresponse will occur. The data are used to guide nonresponse reduction activities, decisions to terminate efforts to increase response rates, as well as statistical models for postsurvey adjustment. The proposed project evaluates the utility of some of these design features for the Health and Retirement Survey (HRS) and the upcoming Asset and Health Dynamics of the Oldest Old (AHEAD). The specific aims of the project are an examination of the characteristics of HRS nonrespondents relative to those of respondents and correlates of HRS nonresponse; construction of alternative postsurvey nonresponse adjustment techniques, evaluated through sensitivity analysis on important HRS analyses and variance properties of the resulting estimators; construction of estimates of nonresponse error for key HRS descriptive statistics; development of cost and error models for nonresponse, evaluating the real tradeoff decisions investigators face between continuing to expend data collection resources to increase response rate and limiting or eliminating further expenditures; and a set of proposed design features for the second wave interviewing of HRS and the AHEAD project informative of nonresponse error features of the studies.